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Fire resistance evaluation through synthetic fire tests and generative adversarial networks

Frontiers of Structural and Civil Engineering 2024年第4期18卷 587-614页

作者： Aybike Özyüksel Çiftçioğlu m.z.naser Department of Civil Engineering Manisa Celal Bayar University45140Türkiye School of Civil&Environmental Engineering and Earth Sciences(SCEEES) Clemson UniversityClemsonSC 29631USA Artificial Intelligence Research Institute for Science and Engineering(AIRISE) Clemson UniversityClemsonSC 29631USA

this paper introduces a machine learning approach to address the challenge of limited data resulting from costly and time-consuming fire experiments by enlarging small fire test data sets and predicting the fire resistance of reinforced concrete *** approach begins by creating deep learning models,namely generative adversarial networks and variational autoencoders,to learn the spatial distribution of real fire *** then use these models to generate synthetic tabular samples that closely resemble realistic fire resistance values for reinforced concrete *** generated data are employed to train state-of-the-art machine learning techniques,including Extreme Gradient Boost,Light Gradient Boosting machine,Categorical Boosting Algorithm,Support Vector Regression,Random Forest,Decision tree,multiple Linear Regression,Polynomial Regression,Support Vector machine,kernel Support Vector machine,naive Bayes,and k-nearest neighbors,which can predict the fire resistance of the columns through regression and *** learning analyses achieved highly accurate predictions of fire resistance values,outperforming traditional models that relied solely on limited experimental *** study highlights the potential for using machine learning and deep learning analyses to revolutionize the field of structural engineering by improving the accuracy and efficiency of fire resistance evaluations while reducing the reliance on costly and time-consuming experiments.

关键词： deep learning fire resistance generative adversarial networks machine learning

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Generation of polarized electron beams through self-injection in the interaction of a laser with a pre-polarized plasma

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High Power Laser Science and Engineering 2024年第3期12卷 62-68页

作者： L.R.Yin X.F.Li Y.J.Gu n.Cao Q.kong m.Büscher S.m.Weng m.Chen z.m.Sheng key Laboratory of nuclear Physics and Ion-beam Application(moE) Institute of Modern PhysicsDepartment of Nuclear Science and TechnologyFudan UniversityShanghaiChina State key Laboratory of High Field Laser Physics Shanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghaiChina SAnkEn(Institute of Scientific and Industrial Research) Osaka UniversityOsakaJapan Sichuan Research Institute Shanghai Jiao Tong UniversityChengduChina Peter Grünberg Institut(PGI-6) Forschungszentrum JülichJülichGermany Institut für Laser-und Plasmaphysik Heinrich-Heine-Universität DüsseldorfDüsseldorfGermany key Laboratory for Laser Plasmas(moE) School of Physics and AstronomyShanghai Jiao Tong UniversityShanghaiChina Collaborative Innovation Center of IFSA Shanghai Jiao Tong UniversityShanghaiChina tsung-Dao Lee Institute Shanghai Jiao Tong UniversityShanghaiChina

Polarized electron beam production via laser wakefield acceleration in pre-polarized plasma is investigated by particlein-cell *** evolution of the electron beam polarization is studied based on the thomas±Bargmann±michel±telegdi equation for the transverse and longitudinal self-injection,and the depolarization process is found to be influenced by the injection *** the case of transverse self-injection,as found typically in the bubble regime,the spin precession of the accelerated electrons is mainly influenced by the ***,in the case of longitudinal injection in the quasi-1D regime(for example,*** et al.,***.110,135002(2013)),the direction of electron spin oscillates in the laser *** the electrons move around the laser axis,the net influence of the laser field is nearly zero and the contribution of the wakefield can be ***,an ultra-short electron beam with polarization of 99%can be obtained using longitudinal self-injection.

关键词： laser wakefield acceleration longitudinal self-injection particle-in-cell simulation polarized electron beam

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Remodeling tumor Immune microenvironment by Using Polymer-Lipid-manganese Dioxide nanoparticles with Radiation therapy to Boost Immune Response of Castration-Resistant Prostate Cancer

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Research 2024年第3期2023卷 83-96页

作者： Abdulmottaleb E.zetrini HoYin Lip Azhar z.Abbasi Ibrahim Alradwan taksim Ahmed Chunsheng He Jeffrey t.Henderson Andrew m.Rauth Xiao Yu Wu Advanced Pharmaceutics and Drug Delivery Laboratory Leslie Dan Faculty of PharmacyUniversity of TorontoM5S 3M2TorontoONCanada. Departments of medical Biophysics and Radiation Oncology University of TorontoM5G 1L7TorontoONCanada.

Despite substantial progress in the treatment of castration-resistant prostate cancer (CRPC), including radiation therapy and immunotherapy alone or in combination, the response to treatment remains poor due to the hypoxic and immunosuppressive nature of the tumor microenvironment. Herein, we exploited the bioreactivity of novel polymer–lipid manganese dioxide nanoparticles (PLmDs) to remodel the tumor immune microenvironment (tImE) by increasing the local oxygen levels and extracellular pH and enhancing radiation-induced immunogenic cell death. this study demonstrated that PLmD treatment sensitized hypoxic human and murine CRPC cells to radiation, significantly increasing radiation-induced DnA double-strand breaks and ultimately cell death, which enhanced the secretion of damage-associated molecular patterns, attributable to the induction of autophagy and endoplasmic reticulum stress. Reoxygenation via PLmDs also polarized hypoxic murine RAW264.7 macrophages toward the m1 phenotype, enhancing tumor necrosis factor alpha release, and thus reducing the viability of murine CRPC tRAmP-C2 cells. In a syngeneic tRAmP-C2 tumor model, intravenous injection of PLmDs suppressed, while radiation alone enhanced recruitment of regulatory t cells and myeloid-derived suppressor cells. Pretreatment with PLmDs followed by radiation down-regulated programmed death-ligand 1 and promoted the infiltration of antitumor CD8+ t cells and m1 macrophages to tumor sites. taken together, tImE modulation by PLmDs plus radiation profoundly delayed tumor growth and prolonged median survival compared with radiation alone. these results suggest that PLmDs plus radiation is a promising treatment modality for improving therapeutic efficacy in radioresistant and immunosuppressive solid tumors.

关键词： RAW264.7 alone treatment

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Prediction of the undrained shear strength of remolded soil with non-linear regression,fuzzy logic,and artificial neural network

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Journal of mountain Science 2024年第9期21卷 3108-3122页

作者： YÜnkÜL kaan kARAÇOR Fatih GÜRBÜz Ayhan BUDAk tahsinÖmür Department of Civil Engineering Faculty of Engineering and ArchitectureTokat Gaziosmanpaşa UniversityTokat 60100Türkiye Department of Civil Engineering Faculty of Engineering and ArchitectureKafkas UniversityKars 36000Türkiye Department of Civil Engineering Faculty of EngineeringGazi UniversityAnkara 06570Türkiye ministry of Youth and Sports Ankara 06090Türkiye

this study aims to predict the undrained shear strength of remolded soil samples using non-linear regression analyses,fuzzy logic,and artificial neural network modeling.A total of 1306 undrained shear strength results from 230 different remolded soil test settings reported in 21 publications were collected,utilizing six different measurement *** water content,plastic limit,and liquid limit were used as input parameters for fuzzy logic and artificial neural network modeling,liquidity index or water content ratio was considered as an input parameter for non-linear regression *** non-linear regression analyses,12 different regression equations were derived for the prediction of undrained shear strength of remolded ***-Forward backpropagation and the tAnSIG transfer function were used for artificial neural network modeling,while the mamdani inference system was preferred with trapezoidal and triangular membership functions for fuzzy logic *** experimental results of 914 tests were used for training of the artificial neural network models,196 for validation and 196 for *** was observed that the accuracy of the artificial neural network and fuzzy logic modeling was higher than that of the non-linear regression ***,a simple and reliable regression equation was proposed for assessments of undrained shear strength values with higher coefficients of determination.

关键词： Undrained shear strength Liquidity index Water content ratio non-linear regression Artificial neural networks Fuzzy logic

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Ground-based and additional science support for SmILE

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Earth and Planetary Physics 2024年第1期8卷 275-298页

作者： J.A.Carter m.Dunlop C.Forsyth k.Oksavik E.Donovon A.kavanagh S.E.milan t.Sergienko R.C.Fear D.G.Sibeck m.Connors t.Yeoman X.tan m.G.G.t.taylor k.mcWilliams J.Gjerloev R.Barnes D.D.Billet G.Chisham A.Dimmock m.P.Freeman D.-S.Han m.D.Hartinger S.-Y.W.Hsieh z.-J.Hu m.k.James L.Juusola k.kauristie E.A.kronberg m.Lester J.manuel J.matzka I.mcCrea Y.miyoshi J.Rae L.Ren F.Sigernes E.Spanswick k.Sterne A.Steuwer t.Sun m.-t.Walach B.Walsh C.Wang J.Weygand J.Wild J.Yan J.zhang Q.-H.zhang Planetary Sciences Group School of Physics and AstronomyUniversity of LeicesterUniversity RoadLeicesterLE17RHUK Rutherford Appleton Laboratory Space Science Technology Facilities CouncilOxfordshireUK School of Space and Environment Beihang UniversityBeijing 100191China key Laboratory of Space Environment monitoring and Information Processing Ministry of Industry and Information TechnologyBeijing 100017China Department of Space and Climate Physics UCLMullard Space Science LaboratoryHolmbury St.MaryDorkingSurreyRH56NTUK Birkeland Centre for Space Science Department of Physics and Technology.University of BergenBergenNorway Department of Physics and Astronomy University of Calgary2500 University DriveCalgaryAlbertaCanada T2N 1N4 British Antarctic Survey CambridgeUK Swedish Institute of Space Physics KirunaSweden West Highfield Campus University of SouthamptonUniversity RoadSO171BJUK Goddard Space Flight Center NASAGreenbeltMarylandUSA Athabasca University AthabascaCanada EStEC European Space AgencyNetherlands University of Saskatchewan SaskatoonCanada the Johns Hopkins University Applied Physics Laboratory LaurelMarylandUSA Swedish Institude of Space Physics UppsalaSE 75121Sweden tongji University Shanghai 200092China Center for Space Plasma Physics Space Science Institute4765 Walnut Street Suite BBoulderColorado80301USA Polar Research Institute of China Shanghai 200136China Finnish meteorological Institute(FmI) Finland Department of Earth and Environmental Sciences(Geophysics) Ludwig Maximilian University of Munich(LMU)MunichTheresienstr.41MunichD-80333Germany Canadian Space Agency MontrealCanada GFz German Research Centre for Geosciences PotsdamGermany nagoya University Institute for Space Earth Environment ResearchCenter for Integrated Data ScienceNagoyaAichiJP 464-8601 northumbria University Newcastle upon TyneTyne and WearUK NE18ST national Space Science Center Chinese Academy SciencesBeijing 100190China Arctic Geophysics University Centre in Svalbard

the joint European Space Agency and Chinese Academy of Sciences Solar wind magnetosphere Ionosphere Link Explorer(SmILE)mission will explore global dynamics of the magnetosphere under varying solar wind and interplanetary magnetic field conditions,and simultaneously monitor the auroral response of the northern Hemisphere *** these large-scale responses with medium and fine-scale measurements at a variety of cadences by additional ground-based and space-based instruments will enable a much greater scientific impact beyond the original goals of the SmILE ***,we describe current community efforts to prepare for SmILE,and the benefits and context various experiments that have explicitly expressed support for SmILE can offer.A dedicated group of international scientists representing many different experiment types and geographical locations,the Ground-based and Additional Science Working Group,is facilitating these *** include constructing an online SmILE Data Fusion Facility,the discussion of particular or special modes for experiments such as coherent and incoherent scatter radar,and the consideration of particular observing strategies and spacecraft *** anticipate growing interest and community engagement with the SmILE mission,and we welcome novel ideas and insights from the solar-terrestrial community.

关键词： magnetosphere ionosphere magnetosphere-ionosphere coupling ground-based experimentation SmILE conjunctions missions

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StCF conceptual design report (Volume 1): Physics & detector

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Frontiers of physics 2024年第1期19卷 1-154页

作者： m.Achasov X.C.Ai L.P.An R.Aliberti Q.An X.z.Bai Y.Bai O.Bakina A.Barnyakov V.Blinov V.Bobrovnikov D.Bodrov A.Bogomyagkov A.Bondar I.Boyko z.H.Bu F.m.Cai H.Cai J.J.Cao Q.H.Cao X.Cao z.Cao Q.Chang k.t.Chao D.Y.Chen H.Chen H.X.Chen J.F.Chen k.Chen L.L.Chen P.Chen S.L.Chen S.m.Chen S.Chen S.P.Chen W.Chen X.Chen X.F.Chen X.R.Chen Y.Chen Y.Q.Chen H.Y.Cheng J.Cheng S.Cheng t.G.Cheng J.P.Dai L.Y.Dai X.C.Dai D.Dedovich A.Denig I.Denisenko J.m.Dias D.z.Ding L.Y.Dong W.H.Dong V.Druzhinin D.S.Du Y.J.Du z.G.Du L.m.Duan D.Epifanov Y.L.Fan S.S.Fang z.J.Fang G.Fedotovich C.Q.Feng X.Feng Y.t.Feng J.L.Fu J.Gao Y.n.Gao P.S.Ge C.Q.Geng L.S.Geng A.Gilman L.Gong t.Gong B.Gou W.Gradl J.L.Gu A.Guevara L.C.Gui A.Q.Guo F.k.Guo J.C.Guo J.Guo Y.P.Guo z.H.Guo A.Guskov k.L.Han L.Han m.Han X.Q.Hao J.B.He S.Q.He X.G.He Y.L.He z.B.He z.X.Heng B.L.Hou t.J.Hou Y.R.Hou C.Y.Hu H.m.Hu k.Hu R.J.Hu W.H.Hu X.H.Hu Y.C.Hu J.Hua G.S.Huang J.S.Huang m.Huang Q.Y.Huang W.Q.Huang X.t.Huang X.J.Huang Y.B.Huang Y.S.Huang n.Hüsken V.Ivanov Q.P.Ji J.J.Jia S.Jia z.k.Jia H.B.Jiang J.Jiang S.z.Jiang J.B.Jiao z.Jiao H.J.Jing X.L.kang X.S.kang B.C.ke m.kenzie A.khoukaz I.koop E.kravchenko A.kuzmin Y.Lei E.Levichev C.H.Li C.Li D.Y.Li F.Li G.Li G.Li H.B.Li H.Li H.n.Li H.J.Li H.L.Li J.m.Li J.Li L.Li L.Li L.Y.Li n.Li P.R.Li R.H.Li S.Li t.Li W.J.Li X.Li X.H.Li X.Q.Li X.H.Li Y.Li Y.Y.Li z.J.Li H.Liang J.H.Liang Y.t.Liang G.R.Liao L.z.Liao Y.Liao C.X.Lin D.X.Lin X.S.Lin B.J.Liu C.W.Liu D.Liu F.Liu G.m.Liu H.B.Liu J.Liu J.J.Liu J.B.Liu k.Liu k.Y.Liu k.Liu L.Liu Q.Liu S.B.Liu t.Liu X.Liu Y.W.Liu Y.Liu Y.L.Liu z.Q.Liu z.Y.Liu z.W.Liu I.Logashenko Y.Long C.G.Lu J.X.Lu n.Lu Q.F.Lü Y.Lu Y.Lu z.Lu P.Lukin F.J.Luo t.Luo X.F.Luo Y.H.Luo H.J.Lyu X.R.Lyu J.P.ma P.ma Y.ma Y.m.ma F.maas S.malde D.matvienko z.X.meng R.mitchell A.nefediev Y.nefedov S.L.Olsen Q.Ouyang P.Pakhlov G.Pakhlova X.Pan Y.Pan E.Passemar Y.P.Pei H.P.Peng L.Peng X.Y.Peng X.J.Peng k.Peters S.Pivovarov E.Pyata B.B.Qi Y.Q.Qi W.B.Qian Y.Qian C.F.Qiao J.J.Qin J.J.Qin L.Q.Qin X.S.Qin t.L.Qiu J.Rademacker C.F.Redmer H.Y.Sang Anhui University Hefei 230039China Beihang University Beijing 100191China Budker Institute of nuclear Physics Novosibirsk 630090Russia CAS key Laboratory of theoretical Physics Institute of Theoretical PhysicsChinese Academy of SciencesBeijing 100190China Cavendish Laboratory University of CambridgeJJ Thomson AveCambridge CB30HEUnited Kingdom Central China normal University Wuhan 430079China Central South University Changsha 410083China China University of Geosciences Wuhan 430074China China University of mining and technology Xuzhou 221116China École Polytechnique Fédérale de Lausanne(EPFL) LausanneSwitzerland Fudan University Shanghai 200433China Goethe University Frankfurt D-60325 Frankfurt am MainGermany Guangxi normal University Guilin 541004China Guangxi Uninversity Nanning 530004China Hangzhou Institute for Advanced Study UCASHangzhou 310024China Hebei normal University Shijiazhuang 050024China Hebei University Baoding 071002China Hefei University of technology Hefei 230601China Helmholtz Institute mainz Staudinger Weg 18D-55099 MainzGermany Henan normal University Xinxiang 453007China Henan University Kaifeng 475004China High Energy Physics Center Chung-Ang UniversitySeoul 06974Korea Higher School of Economy 11 Pokrovsky Bulvar Moscow 109028Russia Huangshan University Huangshan 245000China Hubei University of Automotive technology Shiyan 442002China Hunan normal University Changsha 410081China Hunan University of Science and technology Xiangtan 411201China Hunan University Changsha 410082China Indiana University BloomingtonIndiana 47405USA Inner mongolia University Hohhot 010021China Institute of Advanced Science Facilities Shenzhen 518107China Institute of High Energy Physics Chinese Academy of SciencesBeijing 100049China Institute of modern Physics Chinese Academy of SciencesLanzhou 730000China Institute of Physics Academia SinicaTaipeiTaiwan 11529China Institute of theoretical Physics Chinese Academy of SciencesBeijing 100190China Jilin University Changch

the superτ-charm facility(StCF)is an electron–positron collider proposed by the Chinese particle physics *** is designed to operate in a center-of-mass energy range from 2 to 7 GeV with a peak luminosity of 0.5×10^(35) cm^(–2)·s^(–1) or *** StCF will produce a data sample about a factor of 100 larger than that of the presentτ-charm factory—the BEPCII,providing a unique platform for exploring the asymmetry of matter-antimatter(charge-parity violation),in-depth studies of the internal structure of hadrons and the nature of non-perturbative strong interactions,as well as searching for exotic hadrons and physics beyond the Standard *** StCF project in China is under development with an extensive R&D *** document presents the physics opportunities at the StCF,describes conceptual designs of the StCF detector system,and discusses future plans for detector R&D and physics case studies.

关键词： electron–positron collider tau-charm region high luminosity StCF detector conceptual design

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Search for production of dark fermion candidates in association with heavy neutral gauge boson decaying to dimuon in proton-proton collisions at√s=8 teV using CmS open data

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Chinese Physics C 2024年第4期48卷 6-13页

作者： Y.mahmoud H.Abdallah m.t.Hussein S.Elgammal Centre for theoretical physics The British University in EgyptEgypt Physics Department Faculty of ScienceCairo UniversityEgypt

In this study,we conducted a search for dark matter using a part of the data recorded by the CmS experiment during run-I of the LHC in 2012 with a center of mass energy of 8 teV and an integrated luminosity of 11.6 fb−*** data were gathered from the CmS open *** matter,in the framework of the simplified model(mono-z′),can be produced from proton-proton collisions in association with a new hypothetical gauge boson,z′.thus,the search was conducted in the dimuon plus large missing transverse momentum *** benchmark scenario of mono-z′,which is known as light vector,was used for interpreting the CmS open *** evidence of dark matter was observed,and exclusion limits were set on the masses of dark matter and z′at 95%confidence level.

关键词： dark matter large hadron collider CmS open data search for new physics dark fermion

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Amplitude analysis of the decays D^(0)→π^(+)π^(−)π^(+)π^(−)and D^(0)→π^(+)π^(−)π^(0)π^(0)

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Chinese Physics C 2024年第8期48卷 6-33页

作者： m.Ablikim m.n.Achasov P.Adlarson O.Afedulidis X.C.Ai R.Aliberti A.Amoroso Q.An Y.Bai O.Bakina I.Balossino Y.Ban H.-R.Bao V.Batozskaya k.Begzsuren n.Berger m.Berlowski m.Bertani D.Bettoni F.Bianchi E.Bianco A.Bortone I.Boyko R.A.Briere A.Brueggemann H.Cai X.Cai A.Calcaterra G.F.Cao n.Cao S.A.Cetin J.F.Chang W.L.Chang G.R.Che G.Chelkov C.Chen C.H.Chen Chao Chen G.Chen H.S.Chen m.L.Chen S.J.Chen S.L.Chen S.m.Chen t.Chen X.R.Chen X.t.Chen Y.B.Chen Y.Q.Chen z.J.Chen z.Y.Chen S.k.Choi X.Chu G.Cibinetto F.Cossio J.J.Cui H.L.Dai J.P.Dai A.Dbeyssi R.E.de Boer D.Dedovich C.Q.Deng z.Y.Deng A.Denig I.Denysenko m.Destefanis F.De mori B.Fang S.S.Fang W.X.Fang Y.Fang Y.Q.Fang R.Farinelli L.Fava F.Feldbauer G.Felici C.Q.Feng J.H.Feng Y.t.Feng k.Fischer m.Fritsch C.D.Fu J.L.Fu Y.W.Fu H.Gao Y.n.Gao Yang Gao S.Garbolino I.Garzia P.t.Ge z.W.Ge C.Geng E.m.Gersabeck B.Ding X.X.Ding Y.Ding Y.Ding J.Dong L.Y.Dong m.Y.Dong X.Dong m.C.Du S.X.Du z.H.Duan P.Egorov Y.H.Fan J.Fang JA.Gilman k.Goetzen L.Gong W.X.Gong W.Gradl S.Gramigna m.Greco m.H.Gu Y.t.Gu C.Y.Guan z.L.Guan A.Q.Guo L.B.Guo m.J.Guo R.P.Guo Y.P.Guo A.Guskov J.Gutierrez k.L.Han t.t.Han X.Q.Hao F.A.Harris k.k.He k.L.He F.H.Heinsius C.H.Heinz Y.k.Heng C.Herold t.Holtmann P.C.Hong G.Y.Hou X.t.Hou Y.R.Hou z.L.Hou B.Y.Hu H.m.Hu J.F.Hu t.Hu Y.Hu G.S.Huang k.X.Huang L.Q.Huang X.t.Huang Y.P.Huang t.Hussain F.H\"olzken n.H\"usken n.in der Wiesche m.Irshad J.Jackson S.Janchiv J.H.Jeong Q.Ji Q.P.Ji W.Ji X.B.Ji X.L.Ji Y.Y.Ji X.Q.Jia z.k.Jia D.Jiang H.B.Jiang P.C.Jiang S.S.Jiang t.J.Jiang X.S.Jiang Y.Jiang J.B.Jiao J.k.Jiao z.Jiao S.Jin Y.Jin m.Q.Jing X.m.Jing t.Johansson S.kabana n.kalantar-nayestanaki X.L.kang X.S.kang m.kavatsyuk B.C.ke V.khachatryan A.khoukaz R.kiuchi O.B.kolcu B.kopf m.kuessner X.kui A.kupsc W.k\"uhn J.J.Lane P.Larin L.Lavezzi t.t.Lei z.H.Lei H.Leithoff m.Lellmann t.Lenz C.Li C.Li C.H.Li Cheng Li D.m.Li F.Li G.Li H.Li H.B.Li H.J.Li H.n.Li Hui Li J.R.Li J.S.Li k.Li L.J.Li L.k.Li Lei Li m.H.Li P.R.Li Q.m.Li Q.X.Li R.Li S.X.Li t.Li W.D.Li W.G.Li X.Li X.H.Li X.L.Li X.Y.Li Y Institute of High Energy Physics Chinese Academy of SciencesBeijing 100049China Beihang University Beijing 100191China Bochum Ruhr-University D-44780 BochumGermany Budker Institute of nuclear Physics SB RAS(BInP) Novosibirsk 630090Russia Carnegie mellon University PittsburghPennsylvania 15213USA Central China normal University Wuhan 430079China Central South University Changsha 410083China China Center of Advanced Science and technology Beijing 100190China China University of Geosciences Wuhan 430074China Chung-Ang University Seoul06974Republic of Korea COmSAtS University Islamabad Lahore CampusDefence RoadOff Raiwind Road54000 LahorePakistan Fudan University Shanghai 200433China GSI Helmholtzcentre for Heavy Ion Research GmbH D-64291 DarmstadtGermany Guangxi normal University Guilin 541004China Guangxi University Nanning 530004China Hangzhou normal University Hangzhou 310036China Hebei University Baoding 071002China Helmholtz Institute mainz Staudinger Weg 18D-55099 MainzGermany Henan normal University Xinxiang 453007China Henan University Kaifeng 475004China Henan University of Science and technology Luoyang 471003China Henan University of technology Zhengzhou 450001China Huangshan College Huangshan 245000China Hunan normal University Changsha 410081China Hunan University Changsha 410082China Indian Institute of technology madras Chennai 600036India Indiana University BloomingtonIndiana 47405USA InFn Laboratori nazionali di Frascati INFN Laboratori Nazionali di FrascatiI-00044FrascatiItaly InFn Laboratori nazionali di Frascati INFN Sezione di PerugiaI-06100PerugiaItaly InFn Laboratori nazionali di Frascati University of PerugiaI-06100PerugiaItaly InFn Sezione di Ferrara INFN Sezione di FerraraI-44122FerraraItaly InFn Sezione di Ferrara University of FerraraI-44122FerraraItaly Inner mongolia University Hohhot 010021China Institute of modern Physics Lanzhou 730000China Institute of Physics and technology Peace Avenue 54BUlaanbaatar 13330Mongolia Instituto de A

Using e^(+)e^(−)annihilation data corresponding to an integrated luminosity of 2.93 fb^(−1)taken at the center-of-mass energy√s=3.773 GeV with the BESIII detector,a joint amplitude analysis is performed on the decays D^(0)→π^(+)π^(−)π^(+)π^(−)and D^(0)→π^(+)π^(−)π^(0)π^(0)(non-η).the fit fractions of individual components are obtained,and large interferences among the dominant components of the decays D^(0)→a_(1)(1260)π,D^(0)→π(1300)π,D^(0)→ρ(770)ρ(770),and D^(0)→2(ππ)_(S)are observed in both *** the obtained amplitude model,the CP-even fractions of D^(0)→π^(+)π^(−)π^(+)π^(−)and D^(0)→π^(+)π^(−)π^(0)π^(0)(non-η)are determined to be(75.2±1.1_(stat).±1.5_(syst.))%and(68.9±1.5_(stat).±2.4_(syst.))%,*** branching fractions of D^(0)→π^(+)π^(−)π^(+)π^(−)and D^(0)→π^(+)π^(−)π^(0)π^(0)(non-η)are measured to be(0.688±0.010_(stat.)±0.010_(syst.))%and(0.951±0.025_(stat.)±0.021_(syst.))%,*** amplitude analysis provides an important model for the binning strategy in measuring the strong phase parameters of D^(0)→4πwhen used to determine the Ckm angleγ(ϕ_(3))via the B^(−)→Dk^(−)decay.

关键词： BESIII D^(0)meson decays amplitude analysis CP-even fraction

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Ground-state mass of ^(22)Al and test of state-of-the-art ab initio calculations

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Chinese Physics C 2024年第3期48卷 72-78页

作者：孙铭泽于越王新鹏王猛李健国张玉虎 k.Blaum 陈祖毅陈瑞九邓涵宇付超义葛文文黄文嘉焦红扬李红蕙李宏福罗胤芳廖挺 YuALitvinov 司敏帅鹏史金阳王茜邢元明徐星徐瑚珊许甫荣袁琪 t.Yamaguchi 颜鑫亮杨建成原有进周小红周旭张敏曾奇 CAS key Laboratory of High Precision nuclear Spectroscopy Institute of Modern PhysicsChinese Academy of SciencesLanzhou 730000China School of nuclear Science and technology University of Chinese Academy of SciencesBeijing 100049China max-Planck-Institut für kernphysik Saupfercheckweg 169117 HeidelbergGermany GSI Helmholtzzentrum für Schwerionenforschung Planckstraße 164291 DarmstadtGermany Advanced Energy Science and technology Guangdong Laboratory Huizhou516007China State key Laboratory of nuclear Physics and technology School of PhysicsPeking UniversityBeijing 100871China Department of Physics Saitama UniversitySaitama 338-8570Japan School of nuclear Science and Engineering East China University of TechnologyNanchang 330013China

the ground-state mass excess of the t_(z)=−2 drip-line nucleus ^(22)Al is measured for the first time as 18103(10)keV using the newly-developed Bρ-defined isochronous mass spectrometry method at the cooler storage ring in *** new mass excess value allowed us to determine the excitation energies of the two low-lying 1+states in ^(22)Al with significantly reduced uncertainties of 51 *** compared to the analogue states in its mirror nucleus ^(22)F,the mirror energy differences of the two 1^(+)states in the ^(22)Al-^(22)F mirror pair are determined to be−625(51)keV and−330(51)*** excitation energies and mirror energy differences are used to test the state-of-the-art ab initio valence-space in-medium similarity renormalization group calculations with four sets of interactions derived from the chiral effective field *** mechanism leading to the large mirror energy differences is investigated and attributed to the occupation of theπs_(1/2) orbital.

关键词： nuclear mass Bρ-defined ImS mirror energy difference ab initio VS-ImSRG calculations chiral interactions

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Development,validation,and transportability of several machine-learned,non-exercise-based VO_(2max)prediction models for older adults

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Journal of Sport and Health Science 2024年第5期13卷 611-620页

作者： Benjamin t.Schumacher michael J.Lamonte Andrea z.LaCroix Eleanor m.Simonsick Steven P.Hooker Humberto Parada Jr. John Bellettiere Arun kumar Herbert Wertheim School of Public Health and Human Longevity Science University of California San DiegoLa JollaCA 92093USA Department of Epidemiology and Environmental Health School of Public Health and Health ProfessionsUniversity at BuffaloState University of New YorkBuffaloNY 14214USA translational Gerontology Branch Intramural Research ProgramNational Institute on AgingNational Institutes of HealthBaltimoreMD 21225USA College of Health and Human Services San Diego State UniversitySan DiegoCA 92182USA Division of Epidemiology and Biostatistics School of Public HealthSan Diego State UniversitySan DiegoCA 92182USA University of California San Diego moores Cancer Center La JollaCA 92093USA Computer Science and Engineering and Halicioglu Data Science Institute University of California San DiegoLa JollaCA 92093USA

Background:there exist few maximal oxygen uptake(VO_(2max))non-exercise-based prediction equations,fewer using machine learning(mL),and none specifically for older *** direct measurement of VO_(2max)is infeasible in large epidemiologic cohort studies,we sought to develop,validate,compare,and assess the transportability of several mL VO_(2max)prediction ***:the Baltimore Longitudinal Study of Aging(BLSA)participants with valid VO2_(max)tests were included(n=1080).Least absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine(SVm)algorithms were trained to predict VO_(2max)*** developed these algorithms for:(a)the overall BLSA,(b)by sex,(c)using all BLSA variables,and(d)variables common in aging ***,we quantified the associations between measured and predicted VO_(2max)and ***:the age was 69.0±10.4 years(mean±SD)and the measured VO_(2max)was 21.6±5.9 mL/kg/*** absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine yielded root mean squared errors of 3.4 mL/kg/min,3.6 mL/kg/min,3.4 mL/kg/min,3.6 mL/kg/min,and 3.5 mL/kg/min,*** quartiles of measured VO_(2max)showed an inverse gradient in mortality *** VO_(2max)variables yielded similar effect estimates but were not robust to ***:measured VO_(2max)is a strong predictor of *** mL can improve the accuracy of prediction as compared to simpler approaches but estimates of association with mortality remain sensitive to *** studies should seek to reproduce these results so that VO_(2max),an important vital sign,can be more broadly studied as a modifiable target for promoting functional resiliency and healthy aging.

关键词： Cardiorespiratory fitness Prediction algorithms Epidemiology mortality

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